Dynamic Signal Compression for Robust Motion Vision in Flies

Sensory systems need to reliably extract information from highly variable natural signals. Flies, for instance, use optic flow to guide their course and are remarkably adept at estimating image velocity regardless of image statistics. Current circuit models, however, cannot account for this robustness. Here, we demonstrate that the Drosophila visual system reduces input variability by rapidly adjusting its sensitivity to local contrast conditions. We exhaustively map functional properties of neurons in the motion detection circuit and find that local responses are compressed by surround contrast. The compressive signal is fast, integrates spatially, and derives from neural feedback. Training convolutional neural networks on estimating the velocity of natural stimuli shows that this dynamic signal compression can close the performance gap between model and organism. Overall, our work represents a comprehensive mechanistic account of how neural systems attain the robustness to carry out survival-critical tasks in challenging real-world environments. •Drosophila motion processing robustly estimates the velocity of moving natural scenes•Visual interneurons in the fly visual system dynamically adapt to stimulus contrast•This adaptation relies on fast spatial integration of neural feedback•Contrast adaptation accounts for robust motion vision in computational circuit models Flies reliably estimate the velocity of moving natural scenes regardless of image statistics. Current models of Drosophila motion vision fail to explain this robustness. Drews, Leonhardt, et al. show that flies achieve this performance by rapidly adjusting the sensitivity of visual interneurons in the medulla to surround contrast.